import os
#from langchain_community.chat_models import ChatOpenAI
from langchain_groq import ChatGroq
from dotenv import load_dotenv

load_dotenv()

class LLMHandler:
    def __init__(self, model_name="llama-3.3-70b-versatile"):
        """
        Initializes the LLMHandler with the specified Groq model.
        """
        self.groq_api_key = os.getenv("GROQ_API_KEY")
        if not self.groq_api_key:
            raise ValueError("GROQ_API_KEY environment variable not set.")

        # Initialize Groq LLM client
        self.llm = ChatGroq(groq_api_key=self.groq_api_key, model_name=model_name)

    def generate_response(self, user_prompt, data):
        """
        Generate a concise response using the LLM based on user prompt and data.
        :param user_prompt: Prompt provided by the user.
        :param data: Dictionary containing the instance information (e.g., UID, Name, etc.).
        :return: Generated response text.
        """
        # Create the full prompt using user input and instance data
        prompt = (
            f"You are a professional AI model tasked with writing personalized invite texts "
            f"that are concise (40 to 50 words), brochure-suitable, and tailored as per the user prompt.\n\n"
            f"Consider the user prompt: {user_prompt}\n\n"
            f"Details of the individual:\n"
            f"- Name: {data['Name']}\n"
            f"- Job Title: {data['Job Title']}\n"
            f"- Organisation: {data['Organisation']}\n"
            f"- Area of Interest: {data['Area of Interest']}\n"
            f"- Category: {data['Category']}\n\n"
            f"- Instance info: {data}\n"
            f"- Go through the Instance info If the {user_prompt} mentions you to use variables that are not mentioned above. "
            f"- The response **MUST**:\n"
            f"- Start with 'Hello {data['Name']}'.\n"
            f"- Be concise, professional, and STRICTLY DO NOT generate invalid characters or encoding errors (e.g. 'SoraVR’s').\n"
            f"- Use standard English punctuation, such as single quotes (e.g., 'can't', 'it's').\n"
            f"- STRICTLY Give only one response for the Category the sample belongs to.\n"
            f"- Do NOT include preambles or unnecessary text.\n\n"
            f"Return the final response cleanly, without any extraneous symbols or characters."
        )

        # Query the LLM and return the response
        response = self.llm.invoke(prompt)
        return response.content.strip()